NDLI: Markov-Gibbs model based registration of CT lung images using subsampling for the follow-up assessment of pleural thickenings

Content Provider	IEEE Xplore Digital Library
Author	Faltin, P. Chaisaowong, K. Kraus, T. Aach, T.
Copyright Year	2011
Description	Author affiliation: Institute of Imaging and Computer Vision, RWTH Aachen University, Germany (Faltin, P.; Chaisaowong, K.; Aach, T.) \|\| Institute and Out-patient Clinic of Occupational Medicine, University Hospital Aachen, Germany (Kraus, T.)
Abstract	Examining the growth rate of pleural thickenings in consecutive 3D-CT images requires the matching of identical thickenings in lung images acquired at two different points in time. The thickenings can be subject to strong deformations caused by their growth. This implies that position information should play a major role in finding correspondences. Here, a MGRF approach is presented to determine a rigid transformation. It aligns the lung volumes by maximizing the probability of the regarded lung tissue to fit an offline trained model. To ensure a symmetrical matching of lung surfaces this probability is calculated reciprocally. Using precalculation, strong sub-sampling and a multiscale approach, the required time can be reduced by a factor of about 80, depending on the image resolution. Due to this speed-up, online follow-up assessment is feasible. We show that this approach results in precise registrations which can be used for a reliable matching of lung thickenings.
Starting Page	2181
Ending Page	2184
File Size	2477845
Page Count	4
File Format	PDF
ISBN	9781457713040
ISSN	15224880
e-ISBN	9781457713033
e-ISBN	9781457713026
DOI	10.1109/ICIP.2011.6116066
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-11
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lungs Computed tomography Lattices Conferences Image segmentation CT Markov-Gibbs random field registration multiscale pleuramesothelioma lung
Content Type	Text
Resource Type	Article

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